Sales Forecasting and Inventory Management for Ootdbuy Purchasing Agent in Spreadsheets
2025-04-28
Introduction
In the competitive world of cross-border e-commerce, Ootdbuy purchasing agents face significant challenges in optimizing inventory management. This article explores how to leverage spreadsheet tools to build data-driven sales forecasting models and apply these insights to streamline inventory control—reducing costs while improving capital efficiency.
Methodology
1. Data Preparation
Collect and organize historical sales data in spreadsheets including:
- Daily/weekly/monthly sales quantities
- Product attributes (categories, price points)
- Seasonal markers and promotional flags
- External factors (exchange rates, shipping delays)
2. Time Series Analysis Implementation
Apply spreadsheet functions to detect patterns:
=FORECAST.ETS(target_date, sales_range, date_range, [seasonality], [data_completion], [aggregation])
Key techniques:
- Moving average trends
- Seasonal decomposition (via =STL in Google Sheets)
- Holt-Winters exponential smoothing models
3. Regression Modeling
Build multivariate models to quantify impact factors:
=LINEST(sales_data, (price_range, promo_range, season_range), TRUE, TRUE)
Variables to test:
- Price elasticity coefficients
- Promotion lift factors
- Marketplace traffic correlation
Inventory Optimization Application
Dynamic Replenishment System
Spreadsheet implementation plan:
| SKU | Forecast Demand | Lead Time (days) | Current Stock | Reorder Point |
|---|---|---|---|---|
| OB-JKT-2024 | =C3*1.2 | Calculate animation |
Working Capital Simulation
Tie forecasts to financial planning:
- Project 90-day purchasing budget
- Balance stock levels across product tiers
- Automate alerts via =IF(AND()) conditions
Conclusion
Through proper implementation of time series forecasting and regression analysis in spreadsheets, Ootdbuy agents can achieve: